code: def __init__(
      self,
      in_feats=166,
      n_hidden=76,
      num_layers=2,
      n_classes=2,
      classifier_hidden=510,
  ):
      # default parameters follow the official config
      super(EvolveGCNH, self).__init__()
      self.num_layers = num_layers
      self.pooling_layers = nn.ModuleList()
      self.recurrent_layers = nn.ModuleList()
      self.gnn_convs = nn.ModuleList()
      self.gcn_weights_list = nn.ParameterList()
  
      self.pooling_layers.append(TopK(in_feats, n_hidden))
      # similar to EvolveGCNO
      self.recurrent_layers.append(
          MatGRUCell(in_feats=in_feats, out_feats=n_hidden)
      )
      self.gcn_weights_list.append(
          Parameter(torch.Tensor(in_feats, n_hidden))
      )
      self.gnn_convs.append(
          GraphConv(
              in_feats=in_feats,
              out_feats=n_hidden,
              bias=False,
              activation=nn.RReLU(),
              weight=False,
          )
      )
      for _ in range(num_layers - 1):
          self.pooling_layers.append(TopK(n_hidden, n_hidden))
          self.recurrent_layers.append(
              MatGRUCell(in_feats=n_hidden, out_feats=n_hidden)
          )
          self.gcn_weights_list.append(
              Parameter(torch.Tensor(n_hidden, n_hidden))
          )
          self.gnn_convs.append(
              GraphConv(
                  in_feats=n_hidden,
                  out_feats=n_hidden,
                  bias=False,
                  activation=nn.RReLU(),
                  weight=False,
              )
          )
  
      self.mlp = nn.Sequential(
          nn.Linear(n_hidden, classifier_hidden),
          nn.ReLU(),
          nn.Linear(classifier_hidden, n_classes),
      )
      self.reset_parameters()
  
imports: import torch
  import torch.nn
  from dgl.nn.pytorch import GraphConv
  from torch.nn import init
  from torch.nn.parameter import Parameter
label_desc: Use image node_id 1 to create a new EvolveGCNH object from inherited base classes: nn with example: obj = EvolveGCNH(in_feats, n_hidden, num_layers, n_classes, classifier_hidden)